Data analysis
The SPSS software for Windows (IBM SPSS Statistics 27) was used for
descriptive, correlation, and regression analysis. First, HRV values
that differed from the mean value by more than three standard deviations
were removed. Then, we performed an overall descriptive analysis of the
HRV, questionnaire scores, and demographic information (age, gender).
Pearson correlation was used to calculate the correlation coefficient
between HRV, questionnaire scores, and age. Independent sample t-tests
were used to examine gender differences in HRV and questionnaire scores
and the effects of baseline type (i.e., resting and vanilla) on HRV.
Observed power was estimated by retrospective power analysis to evaluate
the statistical reliability whereas higher power indicates less
probability of type II error. Then, two linear regression analyses were
conducted with HRV as the dependent variable. In model 1, we included
RRStotal as the independent variable and age, gender,
and Depressionstandardized as control variables. In
model 2, we included Brooding and Reflection together with the control
variables (age, gender, and Depressionstandardized). To
explore whether there is a non-linear effect between HRV and rumination
or depression as reported by previous studies, we conducted curve
estimations under regression analyses in SPSS with HRV as the dependent
variable; RRStotal, Brooding, Reflection, the scores
from BDI-II, MASQdepression,
DASSdepression, and
Depressionstandardized as independent variables. One
independent variable was included for each estimation.